Statistical model migration in speaker recognition

Abstract

In large-scale deployments of speaker recognition systems the potential for legacy problems increases as the evolving technology may require configuration changes in the system thus invalidating already existing user voice accounts. Unless the entire database of original speech waveform were stored, users need to reenroll to keep their accounts functional, which, however, may be expensive and commercially not acceptable. We define model migration as a conversion of obsolete models to new-configuration models without additional data and waveform requirements and investigate ways to achieve such a migration with minimum loss of system accuracy. As a proof-of-concept, an algorithm for statistical migration in the Maximum A-Posteriori framework is studied and evaluated experimentally using the NIST SRE-03 dataset. The migration step is discussed in a wider conceptual framework of Conversational Biometrics.